Development and validation of a prognostic model for early triage of patients diagnosed with COVID-19
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SUBMITTER: An C
PROVIDER: S-EPMC8578640 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundThe coronavirus disease 2019 (COVID-19) pandemic demands reliable prognostic models for estimating the risk of long COVID. We developed and validated a prediction model to estimate the probability of known common long COVID symptoms at least 60 days after acute COVID-19.MethodsThe prognostic model was built based on data from a multicentre prospective Swiss cohort study. Included were adult patients diagnosed with COVID-19 between February and December 2020 and treated as outpatients, at ward or intensive/intermediate care unit. Perceived long-term health impairments, including reduced exercise tolerance/reduced resilience, shortness of breath and/or tiredness (REST), were assessed after a follow-up time between 60 and 425 days. The data set was split into a derivation and a geographical validation cohort. Predictors were selected out of twelve candidate predictors based on three methods, namely the augmented backward elimination (ABE) method, the adaptive best-subset selection (ABESS) method and model-based recursive partitioning (MBRP) approach. Model performance was assessed with the scaled Brier score, concordance c statistic and calibration plot. The final prognostic model was determined based on best model performance.ResultsIn total, 2799 patients were included in the analysis, of which 1588 patients were in the derivation cohort and 1211 patients in the validation cohort. The REST prevalence was similar between the cohorts with 21.6% (n = 343) in the derivation cohort and 22.1% (n = 268) in the validation cohort. The same predictors were selected with the ABE and ABESS approach. The final prognostic model was based on the ABE and ABESS selected predictors. The corresponding scaled Brier score in the validation cohort was 18.74%, model discrimination was 0.78 (95% CI: 0.75 to 0.81), calibration slope was 0.92 (95% CI: 0.78 to 1.06) and calibration intercept was -0.06 (95% CI: -0.22 to 0.09).ConclusionThe proposed model was validated to identify COVID-19-infected patients at high risk for REST symptoms. Before implementing the prognostic model in daily clinical practice, the conduct of an impact study is recommended.
| S-EPMC9668400 | biostudies-literature
Project description:Novel coronavirus 2019 (COVID-19) infection is a global public health issue, that has now affected more than 200 countries worldwide and caused a second wave of pandemic. Severe adult respiratory syndrome-CoV-2 (SARS-CoV-2) pneumonia is associated with a high risk of mortality. However, prognostic factors predicting poor clinical outcomes of individual patients with SARS-CoV-2 pneumonia remain under intensive investigation. We conducted a retrospective, multicenter study of patients with SARS-CoV-2 who were admitted to four hospitals in Wuhan, China from December 2019 to February 2020. Mortality at the end of the follow up period was the primary outcome. Factors predicting mortality were also assessed and a prognostic model was developed, calibrated and validated. The study included 492 patients with SARS-CoV-2 who were divided into three cohorts: the training cohort (n = 237), the validation cohort 1 (n = 120), and the validation cohort 2 (n = 135). Multivariate analysis showed that five clinical parameters were predictive of mortality at the end of follow up period, including advanced age [odds ratio (OR), 1.1/years increase (p < 0.001)], increased neutrophil-to-lymphocyte ratio [(NLR) OR, 1.14/increase (p < 0.001)], elevated body temperature on admission [OR, 1.53/°C increase (p = 0.005)], increased aspartate transaminase [OR, 2.47 (p = 0.019)], and decreased total protein [OR, 1.69 (p = 0.018)]. Furthermore, the prognostic model drawn from the training cohort was validated with validation cohorts 1 and 2 with comparable area under curves (AUC) at 0.912, 0.928, and 0.883, respectively. While individual survival probabilities were assessed, the model yielded a Harrell's C index of 0.758 for the training cohort, 0.762 for the validation cohort 1, and 0.711 for the validation cohort 2, which were comparable among each other. A validated prognostic model was developed to assist in determining the clinical prognosis for SARS-CoV-2 pneumonia. Using this established model, individual patients categorized in the high risk group were associated with an increased risk of mortality, whereas patients predicted to be in the low risk group had a higher probability of survival.
| S-EPMC7775455 | biostudies-literature
Project description:ObjectivesThe development of a prognostic mortality risk model for hospitalized COVID-19 patients may facilitate patient treatment planning, comparisons of therapeutic strategies, and public health preparations.MethodsWe retrospectively reviewed the electronic health records of patients hospitalized within a 13-hospital New Jersey USA network between March 1, 2020 and April 22, 2020 with positive polymerase chain reaction results for SARS-CoV-2, with follow-up through May 29, 2020. With death or hospital discharge by day 40 as the primary endpoint, we used univariate followed by stepwise multivariate proportional hazard models to develop a risk score on one-half the data set, validated on the remainder, and converted the risk score into a patient-level predictive probability of 40-day mortality based on the combined dataset.ResultsThe study population consisted of 3123 hospitalized COVID-19 patients; median age 63 years; 60% were men; 42% had >3 coexisting conditions. 713 (23%) patients died within 40 days of hospitalization for COVID-19. From 22 potential candidate factors 6 were found to be independent predictors of mortality and were included in the risk score model: age, respiratory rate ≥25/minute upon hospital presentation, oxygenation <94% on hospital presentation, and pre-hospital comorbidities of hypertension, coronary artery disease, or chronic renal disease. The risk score was highly prognostic of mortality in a training set and confirmatory set yielding in the combined dataset a hazard ratio of 1.80 (95% CI, 1.72, 1.87) for one unit increases. Using observed mortality within 20 equally sized bins of risk scores, a predictive model for an individual's 40-day risk of mortality was generated as -14.258 + 13.460*RS + 1.585*(RS-2.524)^2-0.403*(RS-2.524)^3. An online calculator of this 40-day COVID-19 mortality risk score is available at www.HackensackMeridianHealth.org/CovidRS.ConclusionsA risk score using six variables is able to prognosticate mortality within 40-days of hospitalization for COVID-19.Trial registrationClinicaltrials.gov Identifier: NCT04347993.
| S-EPMC8323891 | biostudies-literature
Project description:In this study, we established a simple and practical tool for early identification of potentially high-risk individuals among elderly COVID-19 patients. Included were 2106 laboratory-confirmed COVID-19 patients aged 60 years and above in 30 provinces of mainland China. Using discrimination (the area under the receiver-operator characteristic curve [AUC]) and calibration (Hosmer-Lemeshow goodness-of-fit test and calibration plots), a nomogram for predicting critically ill cases was developed, and its performance was examined using an internal validation cohort (444 patients) and external cohort (770 patients). The proportion of critically ill patients was 11.8% (248/2106). The most common symptoms at the onset of illness were fever (66.6%), cough (34.1%), fatigue (23.3%), and expectoration (23.6%). Older age, history of chronic obstructive pulmonary disease, fever, fatigue, shortness of breath, and lymphocyte percentage lower than 20% at admission were associated with increased risk of becoming critically ill. The AUCs for the six-variable-based nomogram were 0.77 (95% CI: 0.73-0.82), 0.73 (95% CI: 0.67-0.79), and 0.77 (95% CI: 0.71-0.83) in the development, internal validation, and external validation cohorts, respectively. This six-variable-based nomogram could potentially serve as a practical and reliable tool for early identification of elderly COVID-19 patients at high risk of becoming critically ill.
| S-EPMC7732309 | biostudies-literature
Project description:COVID-19 is a major, urgent, and ongoing threat to global health. Globally more than 24 million have been infected and the disease has claimed more than a million lives as of November 2020. Predicting which patients will need respiratory support is important to guiding individual patient treatment and also to ensuring sufficient resources are available. The ability of six common Early Warning Scores (EWS) to identify respiratory deterioration defined as the need for advanced respiratory support (high-flow nasal oxygen, continuous positive airways pressure, non-invasive ventilation, intubation) within a prediction window of 24 h is evaluated. It is shown that these scores perform sub-optimally at this specific task. Therefore, an alternative EWS based on the Gradient Boosting Trees (GBT) algorithm is developed that is able to predict deterioration within the next 24 h with high AUROC 94% and an accuracy, sensitivity, and specificity of 70%, 96%, 70%, respectively. The GBT model outperformed the best EWS (LDTEWS:NEWS), increasing the AUROC by 14%. Our GBT model makes the prediction based on the current and baseline measures of routinely available vital signs and blood tests.
| S-EPMC8239612 | biostudies-literature
Project description:The sudden deterioration of patients with novel coronavirus disease 2019 (COVID-19) into critical illness is of major concern. It is imperative to identify these patients early. We show that a deep learning-based survival model can predict the risk of COVID-19 patients developing critical illness based on clinical characteristics at admission. We develop this model using a cohort of 1590 patients from 575 medical centers, with internal validation performance of concordance index 0.894 We further validate the model on three separate cohorts from Wuhan, Hubei and Guangdong provinces consisting of 1393 patients with concordance indexes of 0.890, 0.852 and 0.967 respectively. This model is used to create an online calculation tool designed for patient triage at admission to identify patients at risk of severe illness, ensuring that patients at greatest risk of severe illness receive appropriate care as early as possible and allow for effective allocation of health resources.
| S-EPMC7363899 | biostudies-literature
Project description:ObjectiveTo develop a prognostic model to predict disease outcomes in individual patients with Parkinson disease (PD) and perform an external validation study in an independent cohort.MethodsModel development was done in the Comorbidity and Aging in Rehabilitation Patients: The Influence on Activities (CARPA) cohort (Netherlands). External validation was performed using the Cambridgeshire Parkinson's Incidence from GP to Neurologist (CamPaIGN) cohort (UK). Both are longitudinal incident cohort studies that prospectively followed up patients with PD from the time of diagnosis. A composite outcome measure was made in which patients were classified as having an unfavorable prognosis when they had postural instability or dementia at the 5-year assessment (or at the last assessment before loss to follow-up), or had died before this time. The final model was derived with a backward selection strategy from candidate predictor variables that were measured at baseline.ResultsIn the resulting model, higher patient age, higher Unified Parkinson's Disease Rating Scale motor examination axial score, and a lower animal fluency score were all associated with a higher probability of an unfavorable outcome. External validation confirmed good discriminative ability between favorable and unfavorable outcomes with an area under the receiver operating characteristic curve of 0.85 (95% confidence interval 0.77-0.93) and a well-calibrated model with a calibration slope of 1.13 and no significant lack of fit (Hosmer-Lemeshow test: p = 0.39).ConclusionWe constructed a model that allows individual patient prognostication at 5 years from diagnosis, using a small set of predictor variables that can easily be obtained by clinicians or research nurses.
| S-EPMC4799715 | biostudies-literature
Project description:ObjectiveTo develop and validate a prognostic model for early death in patients with traumatic bleeding.DesignMultivariable logistic regression of a large international cohort of trauma patients.Setting274 hospitals in 40 high, medium, and low income countriesParticipantsPrognostic model development: 20,127 trauma patients with, or at risk of, significant bleeding, within 8 hours of injury in the Clinical Randomisation of an Antifibrinolytic in Significant Haemorrhage (CRASH-2) trial. External validation: 14,220 selected trauma patients from the Trauma Audit and Research Network (TARN), which included mainly patients from the UK.OutcomesIn-hospital death within 4 weeks of injury.Results3076 (15%) patients died in the CRASH-2 trial and 1765 (12%) in the TARN dataset. Glasgow coma score, age, and systolic blood pressure were the strongest predictors of mortality. Other predictors included in the final model were geographical region (low, middle, or high income country), heart rate, time since injury, and type of injury. Discrimination and calibration were satisfactory, with C statistics above 0.80 in both CRASH-2 and TARN. A simple chart was constructed to readily provide the probability of death at the point of care, and a web based calculator is available for a more detailed risk assessment (http://crash2.lshtm.ac.uk).ConclusionsThis prognostic model can be used to obtain valid predictions of mortality in patients with traumatic bleeding, assisting in triage and potentially shortening the time to diagnostic and lifesaving procedures (such as imaging, surgery, and tranexamic acid). Age is an important prognostic factor, and this is of particular relevance in high income countries with an aging trauma population.
| S-EPMC3419468 | biostudies-literature
Project description:Importance:Prehospital trauma triage protocols are used worldwide to get the right patient to the right hospital and thereby improve the chance of survival and avert lifelong disabilities. The American College of Surgeons Committee on Trauma set target levels for undertriage rates of less than 5%. None of the existing triage protocols has been able to achieve this target in isolation. Objective:To develop and validate a new prehospital trauma triage protocol to improve current triage rates. Design, Setting, and Participants:In this multicenter cohort study, all patients with trauma who were 16 years and older and transported to a trauma center in 2 different regions of the Netherlands were included in the analysis. Data were collected from January 1, 2012, through June 30, 2014, in the Central Netherlands region for the design data cohort and from January 1 through December 31, 2015, in the Brabant region for the validation cohort. Data were analyzed from May 3, 2017, through July 19, 2018. Main Outcomes and Measures:A new prediction model was developed in the Central Netherlands region based on prehospital predictors associated with severe injury. Severe injury was defined as an Injury Severity Score greater than 15. A full-model strategy with penalized maximum likelihood estimation was used to construct a model with 8 predictors that were chosen based on clinical reasoning. Accuracy of the developed prediction model was assessed in terms of discrimination and calibration. The model was externally validated in the Brabant region. Results:Using data from 4950 patients with trauma from the Central Netherlands region for the design data set (58.3% male; mean [SD] age, 47 [21] years) and 6859 patients for the validation Brabant region (52.2% male; mean [SD] age, 51 [22] years), the following 8 significant predictors were selected for the prediction model: age; systolic blood pressure; Glasgow Coma Scale score; mechanism criteria; penetrating injury to the head, thorax, or abdomen; signs and/or symptoms of head or neck injury; expected injury in the Abbreviated Injury Scale thorax region; and expected injury in 2 or more Abbreviated Injury Scale regions. The prediction model showed a C statistic of 0.823 (95% CI, 0.813-0.832) and good calibration. The cutoff point with a minimum specificity of 50.0% (95% CI, 49.3%-50.7%) led to a sensitivity of 88.8% (95% CI, 87.5%-90.0%). External validation showed a C statistic of 0.831 (95% CI, 0.814-0.848) and adequate calibration. Conclusions and Relevance:The new prehospital trauma triage prediction model may lower undertriage rates to approximately 10% with an overtriage rate of 50%. The next step should be to implement this prediction model with the use of a mobile app for emergency medical services professionals.
| S-EPMC6537785 | biostudies-literature
Project description:BACKGROUND:The current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak is a public health emergency and the case fatality rate in the United Kingdom is significant. Although there appear to be several early predictors of outcome, there are no currently validated prognostic models or scoring systems applicable specifically to patients with confirmed SARS-CoV-2. OBJECTIVE:We aim to create a point-of-admission mortality risk scoring system using an artificial neural network (ANN). METHODS:We present an ANN that can provide a patient-specific, point-of-admission mortality risk prediction to inform clinical management decisions at the earliest opportunity. The ANN analyzes a set of patient features including demographics, comorbidities, smoking history, and presenting symptoms and predicts patient-specific mortality risk during the current hospital admission. The model was trained and validated on data extracted from 398 patients admitted to hospital with a positive real-time reverse transcription polymerase chain reaction (RT-PCR) test for SARS-CoV-2. RESULTS:Patient-specific mortality was predicted with 86.25% accuracy, with a sensitivity of 87.50% (95% CI 61.65%-98.45%) and specificity of 85.94% (95% CI 74.98%-93.36%). The positive predictive value was 60.87% (95% CI 45.23%-74.56%), and the negative predictive value was 96.49% (95% CI 88.23%-99.02%). The area under the receiver operating characteristic curve was 90.12%. CONCLUSIONS:This analysis demonstrates an adaptive ANN trained on data at a single site, which demonstrates the early utility of deep learning approaches in a rapidly evolving pandemic with no established or validated prognostic scoring systems.
| S-EPMC7451108 | biostudies-literature